[1] |
Jiang Chengming, Song Jinhui. An ultrahigh-resolution digital image sensor with pixel size of 50 nm by vertical nanorod arrays [J]. Advanced Materials, 2015, 27(30): 4454-4460. doi: 10.1002/adma.201502079 |
[2] |
Zhang Xiu, Zhou Wei. Image super-resolution reconstruction based on convolution sparse self-encoding [J]. Infrared and Laser Engineering, 2019, 48(1): 0126005. (in Chinese) |
[3] |
Xi Zhihong, Hou Caiyan, Yuan Kunpeng, et al. Accelerated image super-resolution reconstruction based on deep residual network [J]. Acta Optica Sinica, 2019(2): 89-98. |
[4] |
Siu Wan-Chi, Hung Kwok-Wai. Review of image interpolation and super-resolution[C]//Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2012. |
[5] |
Fernandez-Beltran R, Latorre-Carmona P, Pla F. Single-frame super-resolution in remote sensing: A practical overview [J]. International Journal of Remote Sensing, 2017, 38(1): 314-354. doi: 10.1080/01431161.2016.1264027 |
[6] |
Bertero M, Boccacci P. Introduction to Inverse Problems Imaging[M]. Bristol: Institute of Physics Publishing, 1998: 1-8. |
[7] |
Zhao X Q, Jia Y X. An adaptive regularization image super-resolution reconstruction algorithm[C]//2014 33rd Chinese Control Conference (CCC). IEEE, 2014. |
[8] |
Cai Q, Liu Y D, Cao J, et al. A watershed image segmentation algorithm based on self-adaptive marking and interregional affinity propagation clustering [J]. Acta Electronica Sinica, 2017, 45(8): 1911-1918. |
[9] |
Jing L, Liu S, Zhihong L, et al. An image reconstruction algorithm based on the extended Tikhonov regularization method for electrical capacitance tomography [J]. Measurement, 2009, 42(3): 368-376. doi: 10.1016/j.measurement.2008.07.003 |
[10] |
Gou Shuiping, Liu Shuzhen, Wu Yaosheng, et al. Image super-resolution based on the pairwise dictionary selected learning and improved bilateral regularization [J]. IET Image Processing, 2016, 10(2): 101-112. doi: 10.1049/iet-ipr.2015.0046 |
[11] |
Lee E S, Kang M G. Regularized adaptive high-resolution image reconstruction considering inaccurate subpixel registration [J]. IEEE Transactions on Image Processing, 2003, 12(7): 826-37. doi: 10.1109/TIP.2003.811488 |
[12] |
Han Yubing, Wu Lenan, Zhang Dongqing. Super-resolution reconstruction based on regularization processing [J]. Journal of Electronics & Information Technology, 2007, 29(7): 1713-1716. |
[13] |
Marquina A, Osher S J. Image super-resolution by TV-regularization and Bregman iteration [J]. Journal of Scientific Computing, 2008, 37(3): 367-382. doi: 10.1007/s10915-008-9214-8 |
[14] |
Omer O A, Tanaka T. Region-based weighted-norm approach to video super-resolution with adaptive regularization[C]// IEEE International Conference on Acoustics, 2009: 833-836. |
[15] |
Oliveira J P, BioucasDias J M, Figueiredo M A. Adaptive total variation image deblurring: A majorization-minimization approach. [J]. Signal Processing, 2009, 89(9): 1683-1693. doi: 10.1016/j.sigpro.2009.03.018 |
[16] |
Donoho D L. De-noising by soft-thresholding [J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627. doi: 10.1109/18.382009 |
[17] |
Candès Emmanuel J, Wakin M B, Boyd S P. Enhancing sparsity by reweighted l1 minimization [J]. Journal of Fourier Analysis & Applications, 2008, 14(5-6): 877-905. |
[18] |
Jia C, Evans B L. Patch-based image deconvolution via joint modeling of sparse priors[C]//IEEE International Conference on Image Processing, 2011: 681-684. |
[19] |
Dong W, Zhang L, Shi G, et al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization [J]. IEEE Transactions on Image Processing, 2011, 20(7): 1838-1857. doi: 10.1109/TIP.2011.2108306 |
[20] |
David Leigh Donoho. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/TIT.2006.871582 |
[21] |
Mairal Julien. Incremental majorization-minimization optimization with application to large-scale machine learning [J]. SIAM Journal on Optimization, 2015, 25(2): 829-855. doi: 10.1137/140957639 |
[22] |
Papa G, Bianchi P, Clémençon S. Adaptive sampling for incremental optimization using stochastic gradient descent[C]//International Conference on Algorithmic Learning Theory, 2015. |